Optimal Energy Operation Strategy for We-Energy of Energy Internet Based on Hybrid Reinforcement Learning With Human-in-the-Loop

This article investigates the energy operation problem based on We-Energy (WE), a novel full-duplex model in Energy Internet (EI). A dual-objective optimal energy operation model of WE is formulated with the consideration of economical benefit and security operation under different time scenarios. Due to the inaccurate model of distributed generation devices and loads, a multipolicy convex hull reinforcement learning (MCRL) algorithm is proposed. It can find the multiobjective strategy set with model-free feature. Moreover, considering the limitations of artificial intelligence technology and the human advantages in information processing for complex task, a two-channel Human-in-the-loop (HITL) method is designed to combine with MCRL to avoid decision-making risks. The one channel of HITL can evaluate the operation strategy by human under normal conditions so that the understanding of human for complex operating conditions can be incorporated into the machine learning algorithms to improve the confidence of intelligent systems. The other channel of HITL can allow human to participate in real-time adjustment under abnormal conditions to avoid system out of control. Simulation studies of modified EI are confirmed that the proposed algorithm can improve system performance effectively.